Designing a Patient Self-Reporting Stack Around Humans and AI
Every care team I talk to has the same complaint: patients happily text, leave voicemails, and fill out surveys, but those signals rarely make it into the plan of care.
Electronic records were never built to absorb that ambient context, and the people who could act on it are already drowning in portal messages and follow-up calls. Yet the value is obvious, timely symptom reporting keeps people out of the ED, surfaces social needs, and lets providers adjust therapy before a flare turns into a crisis.
What we need is a stack that captures self-reported data, triages it with large language models, and still gives clinicians the last word. The winning pattern blends thoughtful UX, observability, and a human-in-the-loop workflow.